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Record W2565979097 · doi:10.1164/rccm.201605-1026oc

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up

2017· article· en· W2565979097 on OpenAlexafffundabout
Anna Gref, Simon Kebede Merid, Olena Gruzieva, Stéphane Ballereau, Allan B. Becker, Tom Bellander, Anna Bergström, Yohan Bossé, Matteo Bottai, Moira Chan‐Yeung, Elaine Fuertes, Despo Ierodiakonou, Ruiwei Jiang, Stéphane Joly, Meaghan J. Jones, Michael S. Kobor, Michal Korek, Anita L. Kozyrskyj, Ashish Kumar, Nicolas Lemonnier, Elaina MacIntyre, Camille Ménard, David C. Nickle, Ma’en Obeidat, Johann Pellet, Marie Standl, Annika Sääf, Cilla Söderhäll, Carla M. T. Tiesler, Maarten van den Berge, Judith M. Vonk, Hita Vora, Cheng‐Jian Xu, Josep M. Antó, Charles Auffray, Michael Bräuer, Jean Bousquet, Bert Brunekreef, W. James Gauderman, Joachim Heinrich, Juha Kere, Gerard H. Koppelman, Dirkje Postma, Christopher Carlsten, Göran Pershagen, Erik Melén

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité LavalUniversity of AlbertaChild and Family Research InstitutePublic Health OntarioUniversity of British ColumbiaUniversity of Manitoba
FundersCanadian Institutes of Health ResearchInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalCentre National de la Recherche ScientifiqueHjärt-LungfondenNational Cancer InstituteSvenska Forskningsrådet FormasStiftelsen för Strategisk ForskningMünchner Zentrum für Gesundheitswissenschaften, Ludwig-Maximilians-Universität MünchenVetenskapsrådetBritish Columbia Lung AssociationNational Institute of Environmental Health SciencesKarolinska InstitutetManitoba Medical Service FoundationEuropean CommissionStockholms Läns LandstingCancer Research SocietyFonds de Recherche du Québec - SantéUniversité Laval
KeywordsMedicineAsthmaAir pollutionGenomeEnvironmental healthIntensive care medicineGeneticsImmunologyGeneEcology

Abstract

fetched live from OpenAlex

Abstract Rationale The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. Objectives To identify gene–environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. Methods We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors’ diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children’s Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. Measurements and Main Results In the European cohorts, 186 SNPs had an interaction P < 1 × 10−4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10−4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc β-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10−17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. Conclusions Our results indicated that gene–environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.310
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations127
Published2017
Admission routes3
Has abstractyes

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